Skip to main content
ClaudeWave
Skill963 estrellas del repoactualizado 4d ago

debugging-log-analyser

The debugging-log-analyser Claude Code skill systematically parses error logs, stack traces, and crash reports into a structured root cause diagnosis. Use this skill when applications throw exceptions, crash unexpectedly, or produce errors and you need to identify the underlying issue and resolution path. It delivers error classification, frame-by-frame stack trace analysis, probable root cause with confidence assessment, affected code paths, concrete code-level fix suggestions, and prioritized next debugging steps.

Instalar en Claude Code
Copiar
git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skills /tmp/debugging-log-analyser && cp -r /tmp/debugging-log-analyser/plugins/pm-engineering/skills/debugging-log-analyser ~/.claude/skills/debugging-log-analyser
Después abre una sesión nueva de Claude Code; el skill carga automáticamente.

SKILL.md

# Debugging Log Analyser Skill

Parses raw error logs, stack traces, and crash reports into a structured diagnosis with probable root cause, affected code path, and specific next steps — no hand-waving.

## Required Inputs

Ask for these if not provided:
- **The log / stack trace / error output** (paste directly or describe the error)
- **Language and framework** (e.g. Node.js + Express, Python + Django, Java Spring, Go)
- **Context** (what changed before this started — e.g. recent deploy, config change, increased traffic, new input data; or "nothing changed" is also useful)
- **Frequency** (one-off / intermittent / consistent / regression after a specific change)
- **Environment** (local dev / staging / production)
- **What they've already tried** (if anything)

## Output Format

---

# Debugging Report: [Service/App Name]

### 1. Error Classification
**Error type:** [Runtime exception / Build error / Config error / Network error / Memory error / Unknown]
**Severity:** [Fatal / Critical / Warning / Informational]
**Recurrence pattern:** [One-off / Intermittent / Consistent / On-startup / Under load]

### 2. Stack Trace Analysis

Walk the stack frame by frame, starting from the origin:
- **Origin frame:** [File, line, function where it started]
- **Propagation path:** [How it travelled through the call stack]
- **Crash point:** [Where it ultimately threw/panicked/exited]

For each significant frame, note whether it is:
- User code (fixable here)
- Framework/library code (usually a misuse issue)
- System/runtime code (usually a config or environment issue)

### 3. Root Cause Assessment
**Probable root cause:** [1–2 sentence plain English statement]
**Confidence:** [High / Medium / Low — and why]
**Alternative causes to rule out:** [If confidence is not high]

### 4. Affected Code Path
**Entry point:** [Where the triggering call began]
**Key function(s) involved:** [Specific functions/methods named in the trace]
**Data that triggered it:** [If inferable from the log — e.g. null value, malformed JSON]

### 5. Suggested Fix
Provide a concrete, code-level suggestion:
- What to change (the minimal fix)
- Why this fixes the root cause
- Any trade-offs or risks in the fix
- A short code snippet if helpful

### 6. Next Debugging Steps
If the root cause is uncertain, provide an ordered list of 3–5 specific debugging actions:
1. [Specific thing to check — file, log line, config value]
2. [Specific reproduction step or isolation test]
3. [Specific tool command — e.g. `strace`, `pprof`, `--verbose`, add logging at X]

### 7. Prevention
One or two concrete things that would prevent this class of error recurring:
- Better input validation at [point]
- Add monitoring/alerting for [condition]
- Test that covers [scenario]

---

## Quality Checks
- [ ] Root cause is specific (not "there might be a null pointer issue")
- [ ] At least one concrete code-level fix is suggested
- [ ] Next steps are actionable commands, not vague advice
- [ ] Suggested fix references the actual language/framework in the input (not a generic fix that could apply to any language)
- [ ] Confidence level includes a stated reason (not just "High" or "Low" with no explanation)
- [ ] Prevention is proactive (not just "add error handling")

## Usage Examples
- "Why is this crashing?" + [paste log]
- "Can you analyse this stack trace?"
- "I'm getting this error, what does it mean?"
- "Debug this log for me"
- "What's causing this exception?"
ai-ethics-reviewSkill

Conduct a structured ethical review of an AI or ML feature, model, or product. Use when preparing to deploy an AI system, assessing algorithmic risk, auditing a model for bias, or producing a responsible AI impact assessment. Produces a structured ethics review covering fairness, transparency, privacy, safety, accountability, and societal impact with a risk tier score, pre-deployment checklist, and prioritised mitigations.

ai-product-canvasSkill

Structure AI and ML product decisions with the rigour of any product decision. Use when building AI-powered features, evaluating LLM integrations, designing AI products, or assessing AI readiness. Produces a complete AI product canvas covering problem definition, model approach, data requirements, evaluation framework, UX design, responsible AI checklist, and launch monitoring plan.

design-handoff-briefSkill

Transform feature briefs into structured design briefs that give designers the context they need before opening Figma. Use when asked to write a design brief, create a design handoff, brief a designer on a new feature, or translate a PRD into design requirements. Produces a brief with user goal, emotional context, success criteria, constraints, edge cases, and out-of-scope boundaries.

experiment-designerSkill

Design statistically rigorous A/B tests and interpret experiment results. Use when asked to design an experiment, run an A/B test, calculate sample size, interpret test results, or assess whether an experiment was successful. Produces a complete experiment design with hypothesis, sample size, run time, success criteria, and risk flags — or a results interpretation with ship/iterate/kill recommendation.

multi-source-signal-synthesiserSkill

Synthesises user signals from multiple research sources into a unified, weighted insight brief. Use when you have data from interviews, support tickets, NPS verbatims, app reviews, or sales calls and need to reconcile contradictions, surface the underlying need behind requests, or answer 'what are users really telling us'. Produces ranked insights with confidence ratings, source weighting rationale, divergent signal analysis by user segment, and a research gap identification section.

data-analysis-standardSkill

Structure a product data analysis, metric deep-dive, funnel analysis, or cohort study. Use when asked to analyse product metrics, investigate a drop in conversion, explain a data change to stakeholders, or find the root cause of a metric movement. Produces a structured analysis with question, root cause, confidence level, and recommended action.

product-health-analysisSkill

Interpret product metrics against goals and surface actionable signals. Use when asked to analyse product health, review key metrics, investigate a performance issue, produce a health report, or assess product-market fit signals. Produces a structured health report with RAG status, trend analysis, root cause hypotheses, and prioritised actions.

retention-analysisSkill

Structure a retention analysis, churn investigation, or engagement deep-dive for any product team. Use when asked to analyse user retention, investigate churn, measure DAU/MAU, or build a retention improvement plan. Produces a retention snapshot with root cause hypotheses, aha-moment correlation, and prioritised interventions.